ORIGINAL RESEARCH

Meta-analysis of Genome-Wide Association Studiesfor Extraversion: Findings from the Genetics of PersonalityConsortium

Stephanie M. van den Berg1• Marleen H. M. de Moor2,3,4

• Karin J. H. Verweij5,6•

Robert F. Krueger7• Michelle Luciano8,9

• Alejandro Arias Vasquez10,11,12,13•

Lindsay K. Matteson7• Jaime Derringer14

• Tonu Esko15• Najaf Amin16

•

Scott D. Gordon5• Narelle K. Hansell5 • Amy B. Hart17

• Ilkka Seppala18•

Jennifer E. Huffman19• Bettina Konte20

• Jari Lahti21,22• Minyoung Lee23

•

Mike Miller7• Teresa Nutile24

• Toshiko Tanaka25• Alexander Teumer26

•

Alexander Viktorin27• Juho Wedenoja28

• Abdel Abdellaoui2 • Goncalo R. Abecasis29•

Daniel E. Adkins30• Arpana Agrawal31

• Juri Allik32,33• Katja Appel34

•

Timothy B. Bigdeli23• Fabio Busonero35

• Harry Campbell36• Paul T. Costa37

•

George Davey Smith38• Gail Davies8,9

• Harriet de Wit39• Jun Ding67

•

Barbara E. Engelhardt40• Johan G. Eriksson22,41,42,43,44

• Iryna O. Fedko2•

Luigi Ferrucci25• Barbara Franke10,11,12

• Ina Giegling20• Richard Grucza31

•

Annette M. Hartmann20• Andrew C. Heath31

• Kati Heinonen21• Anjali K. Henders5

•

Georg Homuth45• Jouke-Jan Hottenga2

• William G. Iacono7• Joost Janzing11

•

Markus Jokela21• Robert Karlsson27

• John P. Kemp38,46• Matthew G. Kirkpatrick39

•

Antti Latvala41,28• Terho Lehtimaki18

• David C. Liewald8,9• Pamela A. F. Madden31

•

Chiara Magri47• Patrik K. E. Magnusson27

• Jonathan Marten19• Andrea Maschio35

•

Hamdi Mbarek2• Sarah E. Medland5

• Evelin Mihailov15,48• Yuri Milaneschi49

•

Edited by Stacey Cherny.

Stephanie M. van den Berg and Marleen H. M. de Moor shared first

authorship.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10519-015-9735-5) contains supplementarymaterial, which is available to authorized users.

& Stephanie M. van den Berg

stephanie.vandenberg@utwente.nl

1 Department of Research Methodology, Measurement and

Data-Analysis (OMD), Faculty of Behavioural, Management,

and Social Sciences, University of Twente, PO Box 217,

7500 AE Enschede, The Netherlands

2 Department of Biological Psychology, VU University

Amsterdam, Amsterdam, The Netherlands

3 Department of Clinical Child and Family Studies, VU

University Amsterdam, Amsterdam, The Netherlands

4 Department of Methods, VU University Amsterdam,

Amsterdam, The Netherlands

5 QIMR Berghofer Medical Research Institute, Brisbane,

Australia

6 Department of Developmental Psychology and EMGO

Institute for Health and Care Research, VU University

Amsterdam, Amsterdam, The Netherlands

7 Department of Psychology, University of Minnesota,

Minneapolis, USA

8 Department of Psychology, University of Edinburgh,

Edinburgh, UK

9 Centre for Cognitive Ageing and Cognitive Epidemiology,

University of Edinburgh, Edinburgh, UK

10 Donders Institute for Cognitive Neuroscience, Radboud

University Nijmegen, Nijmegen, The Netherlands

11 Department of Psychiatry, Radboud University Nijmegen

Medical Center, Nijmegen, The Netherlands

12 Department of Human Genetics, Radboud University

Nijmegen Medical Center, Nijmegen, The Netherlands

13 Department of Cognitive Neuroscience, Radboud University

Nijmegen Medical Center, Nijmegen, The Netherlands

14 Department of Psychology, University of Illinois at Urbana-

Champaign, Champaign, IL, USA

15 Estonian Genome Center, University of Tartu, Tartu, Estonia

123

Behav Genet (2016) 46:170–182

DOI 10.1007/s10519-015-9735-5

Grant W. Montgomery5• Matthias Nauck50

• Michel G. Nivard2• Klaasjan G. Ouwens2

•

Aarno Palotie51,52• Erik Pettersson27

• Ozren Polasek53• Yong Qian67

•

Laura Pulkki-Raback21• Olli T. Raitakari54,55

• Anu Realo32• Richard J. Rose56

•

Daniela Ruggiero24• Carsten O. Schmidt26

• Wendy S. Slutske57• Rossella Sorice24

•

John M. Starr9,58• Beate St Pourcain38,59,60

• Angelina R. Sutin25,61•

Nicholas J. Timpson38• Holly Trochet19

• Sita Vermeulen12,62• Eero Vuoksimaa28

•

Elisabeth Widen52• Jasper Wouda1,2

• Margaret J. Wright5• Lina Zgaga36,63

•

Generation Scotland64• David Porteous65

• Alessandra Minelli47•

Abraham A. Palmer17,39• Dan Rujescu20

• Marina Ciullo24• Caroline Hayward19

•

Igor Rudan36• Andres Metspalu15,33

• Jaakko Kaprio41,28,52• Ian J. Deary8,9

•

Katri Raikkonen21• James F. Wilson19,36

• Liisa Keltikangas-Jarvinen21•

Laura J. Bierut31• John M. Hettema23

• Hans J. Grabe34,66• Brenda W. J. H. Penninx49

•

Cornelia M. van Duijn16• David M. Evans38

• David Schlessinger67•

Nancy L. Pedersen27• Antonio Terracciano22,25

• Matt McGue7,68•

Nicholas G. Martin5• Dorret I. Boomsma2

Received: 30 October 2014 / Accepted: 10 August 2015 / Published online: 11 September 2015

� The Author(s) 2015. This article is published with open access at Springerlink.com

Abstract Extraversion is a relatively stable and heritable

personality trait associated with numerous psychosocial,

lifestyle and health outcomes. Despite its substantial heri-

tability, no genetic variants have been detected in previous

genome-wide association (GWA) studies, which may be

due to relatively small sample sizes of those studies. Here,

we report on a large meta-analysis of GWA studies for

extraversion in 63,030 subjects in 29 cohorts. Extraversion

item data from multiple personality inventories were har-

monized across inventories and cohorts. No genome-wide

significant associations were found at the single nucleotide

polymorphism (SNP) level but there was one significant hit

at the gene level for a long non-coding RNA site

(LOC101928162). Genome-wide complex trait analysis in

two large cohorts showed that the additive variance

explained by common SNPs was not significantly different

16 Department of Epidemiology, Erasmus University Medical

Center, Rotterdam, The Netherlands

17 Department of Human Genetics, University of Chicago,

Chicago, IL, USA

18 Department of Clinical Chemistry, Fimlab Laboratories and

School of Medicine, University of Tampere, Tampere,

Finland

19 MRC Human Genetics Unit, MRC IGMM, Western General

Hospital, University of Edinburgh, Edinburgh, UK

20 Department of Psychiatry, University of Halle, Halle,

Germany

21 Institute of Behavioural Sciences, University of Helsinki,

Helsinki, Finland

22 Folkhalsan Research Center, Helsinki, Finland

23 Department of Psychiatry, Virginia Institute for Psychiatric

and Behavioral Genetics, Virginia Commonwealth

University, Richmond, VA, USA

24 Institute of Genetics and Biophysics ‘‘A. Buzzati-Traverso’’ –

CNR, Naples, Italy

25 National Institute on Aging, NIH, Baltimore, MD, USA

26 Institute for Community Medicine, University Medicine

Greifswald, Greifswald, Germany

27 Department of Medical Epidemiology and Biostatistics,

Karolinska Institutet, Stockholm, Sweden

28 Department of Public Health, University of Helsinki,

Helsinki, Finland

29 Department of Biostatistics, Center for Statistical Genetics,

University of Michigan School of Public Health, Ann Arbor,

MI, USA

30 Pharmacotherapy & Outcomes Science, Virginia

Commonwealth University, Richmond, VA, USA

31 Department of Psychiatry, Washington University School of

Medicine, St. Louis, MO, USA

32 Department of Psychology, University of Tartu, Tartu,

Estonia

33 Estonian Academy of Sciences, Tallinn, Estonia

34 Department of Psychiatry and Psychotherapy, University

Medicine Greifswald, Greifswald, Germany

35 Istituto di Ricerca Genetica e Biomedica (IRGB), CNR,

Monserrato, Italy

36 Usher Institute for Population Health Sciences and

Informatics, University of Edinburgh, Edinburgh, UK

37 Behavioral Medicine Research Center, Duke University

School of Medicine, Durham, NC, USA

38 Medical Research Council Integrative Epidemiology Unit,

School of Social and Community Medicine, University of

Bristol, Bristol, UK

Behav Genet (2016) 46:170–182 171

123

from zero, but polygenic risk scores, weighted using link-

age information, significantly predicted extraversion scores

in an independent cohort. These results show that

extraversion is a highly polygenic personality trait, with an

architecture possibly different from other complex human

traits, including other personality traits. Future studies are

required to further determine which genetic variants, by

what modes of gene action, constitute the heritable nature

of extraversion.

Keywords Personality � Phenotype harmonization �Common genetic variants � Imputation � Polygenic risk

Introduction

Extraversion is a personality trait characterized by the

tendency to experience positive emotions, to be active and

feel energetic, to be talkative and to enjoy social interac-

tions. Extraversion is associated with numerous psy-

chosocial, lifestyle and health outcomes, such as academic

and job performance, well-being, obesity, substance use,

physical activity, bipolar disorder, borderline personality

disorder, Alzheimer’s disease, and longevity (De Moor

et al. 2006, 2011; Distel et al. 2009a; Furnham et al. 2013;

Judge et al. 2013; Middeldorp et al. 2011; Rhodes and

Smith 2006; Sutin et al. 2011; Terracciano et al. 2008;

Terracciano et al. 2014; Weiss et al. 2008).

Extraversion can be measured with multiple inventories

that have been developed as part of different personality

theories. For example, extraversion is one of the five per-

sonality domains as assessed with the Neuroticism–Ex-

traversion–Openness to Experience (NEO) personality

inventories (Costa and McCrae 1992). Extraversion is also

included in Eysenck’s three-dimensional theory of per-

sonality (Eysenck and Eysenck 1964, 1975; Eysenck et al.

1985). In Cloninger’s theory on temperaments and char-

acters (Cloninger 1987; Cloninger et al. 1993), Harm

Avoidance, Novelty Seeking and Reward Dependence are

related to extraversion (De Fruyt et al. 2000). Tellegen’s

personality theory posits the higher order domain of Posi-

tive Emotionality (Patrick et al. 2002), which resembles

and is highly correlated with extraversion (Church 1994).

We showed recently, by performing an Item Response

Theory (IRT) analysis using test linking (Kolen and

Brennan 2004), that item data on Extraversion, Reward

dependence and Positive Emotionality can be harmonized

to broadly assess the same underlying extraversion con-

struct (van den Berg et al. 2014). This harmonization was

performed in over 160,000 individuals from 23 cohorts

participating in the Genetics of Personality Consortium

(GPC). Briefly, harmonization was carried out in each

cohort separately by first fitting an IRT model to data from

39 Department of Psychiatry and Behavioral Neuroscience,

University of Chicago, Chicago, USA

40 Department of Biostatistics and Bioinformatics, Duke

University, Durham, NC, USA

41 National Institute for Health and Welfare (THL), Helsinki,

Finland

42 Department of General Practice and Primary Health Care,

University of Helsinki, Helsinki, Finland

43 Unit of General Practice and Primary Health Care, University

of Helsinki, Helsinki, Finland

44 Vasa Central Hospital, Vaasa, Finland

45 Interfaculty Institute for Genetics and Functional Genomics,

University of Greifswald, Greifswald, Germany

46 Translational Research Institute, University of Queensland

Diamantina Institute, Brisbane, Australia

47 Department of Molecular and Translational Medicine,

University of Brescia, Brescia, Italy

48 Department of Biotechnology, University of Tartu, Tartu,

Estonia

49 Department of Psychiatry, EMGO? Institute, Neuroscience

Campus Amsterdam, VU University Medical Center,

Amsterdam, The Netherlands

50 Institute of Clinical Chemistry and Laboratory Medicine,

University Medicine Greifswald, Greifswald, Germany

51 Wellcome Trust Sanger Institute, Wellcome Trust Genome

Campus, Hinxton, Cambridge, UK

52 Institute for Molecular Medicine Finland (FIMM), University

of Helsinki, Helsinki, Finland

53 Department of Public Health, Faculty of Medicine,

University of Split, Split, Croatia

54 Department of Clinical Physiology and Nuclear Medicine,

Turku University Hospital, Turku, Finland

55 Research Centre of Applied and Preventive Cardiovascular

Medicine, University of Turku, Turku, Finland

56 Department of Psychological & Brain Sciences, Indiana

University, Bloomington, IN, USA

57 Department of Psychological Sciences and Missouri

Alcoholism Research Center, University of Missouri,

Columbia, MO, USA

58 Alzheimer Scotland Dementia Research Centre, University of

Edinburgh, Edinburgh, UK

59 School of Oral and Dental Sciences, University of Bristol,

Bristol, UK

60 School of Experimental Psychology, University of Bristol,

Bristol, UK

61 College of Medicine, Florida State University, Tallahassee,

FL, USA

172 Behav Genet (2016) 46:170–182

123

individuals who had completed at least two different per-

sonality questionnaires. Next, based on calibrated item

parameters, personality scores were estimated based on all

available data for each individual, irrespective of what

personality questionnaire was used. The harmonized

extraversion phenotype was heritable. A broad-sense heri-

tability of 49 % was estimated, based on a meta-analysis in

six twin cohorts that are included in the GPC (29,501 twin

pairs), of which 24 % was due to additive genetic variance

and 25 % due to non-additive genetic variance. The broad-

sense heritability estimate is similar to heritability esti-

mates obtained for extraversion as assessed with single

measurement instruments (Bouchard and Loehlin 2001;

Distel et al. 2009b; Finkel and McGue 1997; Keller et al.

2005; Rettew et al. 2008; Yamagata et al. 2006). Some

evidence for qualitative sex differences in the genetic

influences on extraversion was suggested by a genetic

correlation in opposite-sex twin pairs of 0.38 (van den Berg

et al. 2014). Extraversion becomes more genetically stable

during adolescence until it is almost perfectly genetically

stable in adulthood (Briley and Tucker-Drob 2014; Kandler

2012), that is, the same genes are responsible for

extraversion measured at different ages.

A handful of genome-wide association (GWA) studies for

extraversion have been published, aimed at detecting

specific single nucleotide polymorphisms (SNPs) that

explain part of the heritability. The first GWA study for

personality, which focused on the five NEO personality

traits, was conducted in 3972 adults (Terracciano et al.

2010). No genome-wide significant SNP associations were

found for extraversion, although some interesting associa-

tions with P-values \10-5 were seen with SNPs in two

cadherin genes and the brain-derived neurotrophic factor

(BDNF) gene. A subsequent meta-analysis of GWA results

for the NEO personality traits, conducted in 17,375 subjects,

also did not yield any genome-wide significant associations

for extraversion (De Moor et al. 2012). Two other GWA

studies reported a similar lack of genome-wide significance

for Cloninger’s temperament scales (Service et al. 2012;

Verweij et al. 2010). Interestingly, a study that performed a

genetic complex trait analysis (GCTA; Yang et al. 2010) for

neuroticism and extraversion in around 12,000 unrelated

individuals reported that 12 % (SE = 3 %) of the variance

in extraversion was explained by common SNPs of additive

effect (Vinkhuyzen et al. 2012). Taken together, the results

from twin and genome-wide studies suggest that common

SNPs of additive effect are important, that genetic non-ad-

ditivity may play a role, and that large sample sizes are likely

to be required to identify specific variants.

In this paper, we report the results of the largest meta-

analysis of GWA results for extraversion so far, carried out

in 29 cohorts that participate in the GPC. A total of 63,030

subjects with harmonized extraversion and genome-wide

genotype data were included in the meta-analysis. A 30th

cohort was used for replication. In this consortium we

reported earlier on a genome-wide significant hit for neu-

roticism (De Moor et al. 2015), indicating that we may

begin to analyze data from sufficiently large samples, to

obtain the first significant findings from GWA studies for

personality. In addition to meta-analysis of GWA results,

we computed weighted polygenic scores in an independent

cohort and associated them with extraversion, and esti-

mated variance explained by SNPs in two large cohorts.

Materials and methods

Cohorts

The full meta-analysis was performed on 63,030 subjects

from 29 discovery cohorts. All samples were of European

origin. Twenty-one cohorts were from Europe, six from the

United States and two from Australia. Sample sizes of the

individual cohorts ranged from 177 to 7210 subjects. Please

note that some cohorts were also part of previously published

GWA studies on extraversion. The Generation Scotland:

Scottish Family Health Study (GS:SFHS) cohort was

included as a replication sample (9,783 subjects). A brief

overview of all cohorts is provided in Table 1. A description

of each individual cohort is found in the Supplementary

materials and methods (see also De Moor et al. 2015).

Phenotyping

A harmonized latent extraversion score was estimated for

all participants in all 29 cohorts that were included in the

GWA meta-analysis. This score was based on all available

extraversion item data for each individual (for a detailed

62 Department for Health Evidence, Radboud University

Medical Center, Nijmegen, The Netherlands

63 Department of Public Health and Primary Care, Trinity

College Dublin, Dublin, Ireland

64 Scottish Family Health Study, A Collaboration Between the

University Medical Schools and NHS,

Aberdeen, Dundee, Edinburgh and Glasgow, UK

65 Medical Genetics Section, Centre for Genomics and

Experimental Medicine, Institute of Genetics and Molecular

Medicine, Western General Hospital, The University of

Edinburgh, Edinburgh, UK

66 Department of Psychiatry and Psychotherapy, HELIOS

Hospital Stralsund, Stralsund, Germany

67 Laboratory of Genetics, National Institute on Aging, National

Institutes of Health, Baltimore, MD, USA

68 Institute of Public Health, University of Southern Denmark,

Odense, Denmark

Behav Genet (2016) 46:170–182 173

123

description see van den Berg et al. 2014). Extraversion

item data came from the extraversion scales of the NEO

Personality Inventory, the NEO Five Factor Inventory, the

50-item Big-Five version of the International Personality

Item Pool inventory, the Eysenck Personality Question-

naire and the Eysenck Personality Inventory, from the

Reward Dependence scale of the Cloninger’s Tridimen-

sional Personality Questionnaire, and from the Positive

Emotionality scale of the Multidimensional Personality

Questionnaire (see van den Berg et al. 2014 and Supple-

mentary materials and methods). In the GS:SFHS cohort

that was included for replication of top signals, extraver-

sion was based on the summed score of the extraversion

scale of the EPQ Revised Short Form.

Genotyping and imputation

Genotyping in all cohorts was carried out on Illumina or

Affymetrix platforms, after which quality control (QC) was

performed, followed by imputation of genotypes. QC of

genotype data was performed in each cohort separately,

with comparable but cohort specific criteria. Standard QC

checks included tests of European ancestry, sex inconsis-

tencies, Mendelian errors, and high genome-wide

homozygosity. Checks for relatedness were conducted in

those cohorts that aimed to include unrelated individuals

only. Other checks of genotype data were based on minor

allele frequencies (MAF), SNP call rate (% of subjects with

missing genotypes per SNP), sample call rate (% of miss-

ing SNPs per subject) and Hardy–Weinberg Equilibrium

(HWE). Genotype data were imputed using the 1000Gen-

omes phase 1 version 3 (build37, hg19) reference panel

with standard software packages such as IMPUTE, MACH,

or Minimac, see Supplementary Table 1.

Statistical analyses

GWA analysis per cohort

GWA analyses were conducted independently in each

cohort. Since the cohorts used different research designs

(case–control, population twin studies, extended pedigrees,

etc.), GWA methods were optimized for each cohort.

Extraversion scores were regressed on each SNP under an

additive model, with sex and age included as covariates.

Covariates such as ancestry Principal Components (PCs)

were added if deemed necessary for a particular cohort. In

all analyses, the uncertainty of the imputed genotypes was

taken into account, either using dosage scores or mixtures

of distributions. In those cohorts that included related

individuals, the dependency among participants was

accounted for using cohort-specific methods. Standard

software packages for GWA analyses were used (see

Supplementary Table 1).

Meta-analysis of GWA results across cohorts

A meta-analysis of the GWA results was conducted

with the weighted inverse variance method in METAL

(http://www.sph.umich.edu/csg/abecasis/metal/index.html).

Excluded from meta-analysis were poorly imputed SNPs

(r2\ 0.30 or proper_info\ 0.40) and SNPs with low

Table 1 Overview of 29 discovery cohorts and 1 replication cohort

of the Genetics of Personality Consortium

Cohort # Subjectsa # SNPsb

1 ALSPAC 4705 6,454,153

2 BLSA 820 4,989,411

3 BRESCIA 177 3,549,919

4 CHICAGO 311 3,755,416

5 CILENTO 627 1,123,089

6 COGA 647 5,127,101

7 COGEND 1279 5,932,838

8 EGCUT 1184 5,574,695

9 ERF 2300 5,142,865

10 FTC EPI 567 4,870,096

11 FTC NEO 813 5,092,018

12 HBCS 1456 5,612,790

13 CROATIA-Korcula 808 5,094,034

14 LBC1921 437 4,363,611

15 LBC1936 952 5,168,754

16 MCTFR 7099 6,569,999

17 MGS 2101 5,900,898

18 NBS 1832 5,603,447

19 NESDA 2227 4,707,569

20 NTR 6416 5,339,798

21 ORCADES 1650 4,265,590

22 PAGES 476 4,547,293

23 QIMR adolescents 2842 5,957,064

24 QIMR adults 7210 6,343,920

25 SardiNIA 4332 6,291,135

26 SHIP 2213 5,913,428

27 STR 4903 6,519,094

28 CROATIA-Vis 909 5,327,671

29 YFS 1737 5,914,679

Total 63,030 7,460,147

30 GS:SFHS 9783 74

NA Not Applicable for replication cohort because only top hits were

sought to replicatea Number of subjects with valid latent score for Extraversion and

SNP data (after imputation and cleaning)b Number of SNPs (after imputation and cleaning) with valid asso-

ciation results that entered the meta-analysis

174 Behav Genet (2016) 46:170–182

123

MAF (MAF\H(5/N), which corresponds to less than 5

estimated individuals in the least frequent genotype group,

under the assumption of HWE). This resulted in a total

number of 7,460,147 unique SNPs in the final meta-anal-

ysis (with 1.1–6.6 M SNPs across cohorts). For 2182 SNPs,

SNP locations could not be matched with rs names. For an

additional 516,362 SNPS, results were based on one cohort

only and therefore left out of the analysis, so that the results

are based on 6,941,603 SNPs. Genomic control inflation

factors (lambda), Manhattan plots and quantile–quantile

plots per cohort are provided in Supplementary Table 2

and Supplementary Figs. 1, 2. A P value of 5 9 10-8 was

used as the threshold for genome-wide significance.

The meta-analysis results (P-values per SNP) were used

as the input to compute P-values at the gene level. We

performed these analyses in KGG (Li et al. 2012). A P-

value of 2.87 9 10-6 was used as the threshold for gen-

ome-wide significance in these gene-wide analyses, based

on controlling for the false-discovery rate (Benjamini and

Hochberg 1995).

All GWAS SNP top hits with a P-value smaller than

1 9 10-5 were selected for replication in the GS:SFHS

cohort.

Polygenic risk score analysis

Additional analyses were conducted to test whether

extraversion could be predicted in an independent target

cohort based on the GWA meta-analysis results. The target

cohort was the Netherlands Twin Register (NTR) cohort

(8648 subjects). Polygenic risk scores for this cohort were

estimated using LDpred (Vilhjalmsson et al. 2015) that

takes into account linkage disequilibrium among the SNPs.

The estimation was based on a GWA meta-analysis in

which the NTR and NESDA cohorts were excluded (fur-

ther referred to as the discovery set). With the LD-cor-

rected polygenic risk scores, generalized estimating

equation (GEE) modeling was applied to test whether the

polygenic risk scores predicted extraversion in the target

cohort. The covariates age, sex and ten PCs were included

as fixed effects in the model. The model also included a

random intercept with family number as the cluster vari-

able, to account for dependency among family members.

Outliers on the PCs, including ethnic outliers, were

excluded from the analysis.

Variance explained by SNPs

In the NTR cohort and the QIMR Berghofer Medical

Research Institute (QIMR) adult cohort (see also Supple-

mentary materials and methods), GCTA software (Visscher

et al. 2010; Yang et al. 2010) was used to estimate the

proportion of variance in extraversion that can be explained

by common SNPs of additive effect. In the NTR, this

analysis was carried out in a set of 3597 unrelated indi-

viduals and in the QIMR adult cohort this was done in 3369

unrelated individuals (in each cohort one member per

family was selected with harmonized extraversion and

genome-wide SNP data). GCTA analysis was based on best

guess genotypes obtained in PLINK using a threshold of a

maximum genotype probability [0.70, and additionally

filtering on r-squared[0.80. Next, in estimating the GRM

matrix in the GCTA software, SNPs with MAF\0.05 were

excluded. The additive genetic relationship matrices

(GRM) estimated based on SNPs for all individuals formed

the basis to estimate the proportion of phenotypic variance

explained by SNPs in the NTR and QIMR cohorts. In other

words, it was determined to what extent phenotypic simi-

larity between individuals corresponds to genetic similarity

(at the SNP level). For both NTR and QIMR, sex, age and a

set of population-specific PCs were included as covariates.

Results

Meta-analysis of GWA results

Meta-analysis of GWA results across the 29 discovery

cohorts did not yield genome-wide significant SNPs asso-

ciated with extraversion. The lowest P-value observed was

2.9 9 10-7 for a SNP located on chromosome 2. There

were 74 SNPs with P-values \1 9 10-5. The Manhattan

and quantile–quantile plots are provided in Figs. 1 and 2. A

list with the top five SNPs is given in Table 2. A list with

all SNPs that reached the level of suggestive genome-wide

significance (P\ 1 9 10-5) is found in Supplementary

Table 3. The results of all SNPs can be downloaded from

www.tweelingenregister.org/GPC. A gene-based test

showed one significant hit for LOC101928162, a long non-

coding RNA site, P = 2.87 9 10-6. A list with the top five

genes from the gene-based analysis is provided in Table 3.

Supplementary Table 4 provides the top 30 genes. Among

the top 30 genes was Brain-Derived Neurotrophic Factor

(BDNF, P = 0.0003), a gene also implicated, though not

genome-wide significant, in Terracciano et al. (2010), as

was the BDNF anti-sense RNA gene (P = 0.0001).

Results of the follow-up analysis of the top five SNPs in

the GS:SFHS cohort can be found in Table 2. Of the top

five SNPs, none showed a significant effect. For an over-

view of the replication results of all top SNPs with P-value

\1 9 10-5 see Supplementary Table 3. Of the 74 SNPs

tested in the replication cohort, three SNPs showed nomi-

nal evidence of association (P\ 0.05), which is less than

the number expected based on chance alone

(0.05 9 74 = 3.7).

Behav Genet (2016) 46:170–182 175

123

Polygenic risk score analysis

There were 8201 persons individuals with polygenic scores

for prediction of extraversion. The LDpred-based genetic

risk scores significantly predicted extraversion in the target

cohort, B = 0.059, X2(1) = 27.30, P\ 0.001.

Variance explained by SNPs

In the NTR cohort, an estimated 5.0 % (SE = 7.2) of the

variance in extraversion was explained by all SNPs, but

this estimate was not significantly different from zero

(P = 0.24). In the QIMR cohort, 0.0001 % (SE = 15) of

the variance was explained by SNPs (P = 0.46).

Discussion

This study assessed the influence of common genetic

variants on extraversion in 63,030 individuals from 29

cohorts in the GPC. First, a meta-analysis of GWA anal-

yses across 29 discovery cohorts showed no genome-wide

significant SNPs. Top SNPs detected in the meta-analysis

of GWA results in the discovery phase were not replicated

in the GS:SFHS cohort. The SNPs with lowest P-values

have no previously reported relationship with personality,

psychopathology or brain functioning. Polygenic risk

scores based on the meta-analysis results predicted

extraversion in an independent data set. SNP-based

Fig. 1 Manhattan plot for meta-analysis results of 29 discovery cohorts for extraversion in the Genetics of Personality Consortium

Fig. 2 Quantile-Quantile plots for meta-analysis results of 29

discovery cohorts for extraversion in the Genetics of Personality

Consortium

Table 2 Top SNPs from the meta-analysis of GWA results in 29 discovery cohorts for extraversion, and their replication in the GS:SFHS

cohort, in the Genetics of Personality Consortium

SNP Chr_BP Alleles Closest gene Discovery results Replication results

Effect SE P-value Effect SE P-value

rs2024488 2_217662968 A/G LOC101928250 -0.0303 0.0059 2.939 x 10-7 0.0285 0.0164 0.08244

rs2712162 2_217661788 T/C LOC101928250 -0.0300 0.0059 3.872 x 10-7 0.0278 0.0164 0.08947

rs797182 12_10900487 A/G \NA[ -0.0277 0.0056 6.673 x 10-7 -0.0135 0.0153 0.37721

rs8010306 14_37150160 A/G SLC25A21 0.0629 0.0128 8.730 x 10-7 0.0180 0.0314 0.56650

rs117292860 19_2227621 A/C DOT1L 0.0553 0.0113 9.191 x 10-7 -0.0350 0.0239 0.14368

176 Behav Genet (2016) 46:170–182

123

heritabilities for extraversion were not significantly dif-

ferent from zero in two large cohorts of the GPC.

Although there were no genome-wide significant results

for individual SNPs, in the gene-based analysis, there was

a significant hit for one locus, LOC101928162. This is

long noncoding RNA site whose function remains elusive.

Interestingly, among the top 30 genes were genes previ-

ously implicated in extraversion or in psychiatric disorders

associated with extraversion. The low P-value for

CRTAC1 (P = 2.97 x 10-5), harks back to an interesting

extraversion SNP (rs7088779) in a previous GWAS on

personality (Amin et al. 2013) that is located

between CRTAC1 and C10orf28. RELN (P = 5.69 x 10-5)

has been reported to increase the risk for schizophrenia and

bipolar disorder (Kuang et al. 2011; Ovadia and Shifman

2011), while ADAM12 (7.65 x 10-5) was previouslyfound

to be involved in schizophrenia (Farkas et al. 2010),

and bipolar disorder treatment (Nadri et al.

2007). The BDNF gene was also implicated in a previous

extraversion GWAS (Terracciano et al. 2010), though not

genome-wide significant. Liu et al. (2005) reported a trend

towards association of BDNF variants with substance

abuse, Jiao et al. (2011) reported an association with obe-

sity, and Lang et al. (2007) and Beuten et al. (2005) re-

ported associations with smoking behavior. As

extraversion is known to be associated with lifestyle,

obesity and substance abuse, we deem BDNF to be an

interesting candidate gene for extraversion in future stud-

ies, along with CRTAC, ADAM12 and RELN.

With the current meta-analysis we more than tripled the

sample size as compared to the largest previously published

meta-analysis for extraversion (De Moor et al. 2012). In

contrast to neuroticism, no genome-wide significant SNPs

were found. Some have argued (Turkheimer et al. 2014) that

the heritability of personality traits represents nonspecific

genetic background, which is composed of so many genetic

variants with extremely small effect sizes that individually

these have no causal biological interpretation. It may be that

extraversion differs in this respect from neuroticism. One

other difference was indicated from the analyses of the IRT-

based extraversion and neuroticism scores: whereas for

neuroticism no evidence for genotype x sex interaction was

seen (van den Berg et al. 2014), for extraversion there was

significant evidence for sex limitation. It also is interesting to

note that despite the fact that for extraversion no genome-

wide significant findings emerged for single SNPs, we were

able to predict extraversion in an independent dataset, based

on the polygenic risk cohorts from the discovery set. This

indicates that some true signal is entailed in the meta-anal-

ysis results.

The results of the polygenic risk score analysis are in

contrast with the results from the GCTA analysis, in which

no significant proportion of variance explained by SNPs

was detected in two large cohorts of the GPC. Our study on

neuroticism reported a SNP-based heritability of 15 % (De

Moor et al. 2015). The current extraversion GCTA findings

are also somewhat at odds with two previous GCTA studies

for personality traits. One study focused on neuroticism

and extraversion as measured with different instruments in

four cohorts, and found on average 12 % explained vari-

ance for extraversion, although across cohorts these esti-

mates varied widely (0–27 %) (Vinkhuyzen et al. 2012).

Estimates for neuroticism also varied, but were generally

lower than for extraversion in this study, with an average of

6 % explained variance. In another study, between 4.2 and

9.9 % of explained variances were found for the four

Cloninger temperaments in a combined sample of four

cohorts (Verweij et al. 2012). The proportions of variances

for Harm Avoidance, Novelty Seeking and Persistence

were significant at P\ 0.05, whereas interestingly the

proportion of variance for Reward Dependence was not. It

should be noted that both these studies included the QIMR

cohort in their analyses, so there is some overlap in sub-

jects across studies. The difference is that in the earlier

studies extraversion and reward dependence were based on

single personality inventories, while in our study

extraversion scores harmonized among different personal-

ity inventories were analyzed. What our results and the

results in the previous studies have in common though, is

that the estimates are considerably smaller than the heri-

tability estimates based on twin studies. Given that about

half of the heritability of extraversion consists of non-ad-

ditive genetic variance (van den Berg et al. 2014), it is not

unlikely that this discrepancy is caused by the influence of

Table 3 Top genes from the meta-analysis of GWA results in 29 discovery cohorts for Extraversion in the Genetics of Personality Consortium

Gene Full gene name Pathways P-value

LOC101928162 [Long non-coding RNA] Unknown 0.00000287

LOC729506 [Long non-coding RNA] Unknown 0.00000893

PLEKHJ1 Pleckstrin Homology Domain Containing, Family J

Member 1

Phospholipid binding, circadian clock

functioning

0.0000132

POU2F3 POU Class 2 Homeobox 3 Influenza A 0.0000179

CRTAC1 Cartilage Acidic Protein 1 Unknown 0.0000297

Behav Genet (2016) 46:170–182 177

123

common variants that interact within loci (dominance) or

across loci (epistasis). In addition, the influence of rare

variants may be implicated. The relatively limited influ-

ence of common additive genetic variation, as well as a

previously reported finding that higher levels of inbreeding

are associated with less socially desirable personality trait

levels, has led to the idea that the genetic variation in

personality traits may have been maintained by mutation–

selection balance (Verweij et al. 2012), and our results are

consistent with this idea.

This study comes with some limitations. Genotyping,

QC, and imputation were carried out separately in each

cohort. Any difference in procedures may have caused

some loss of statistical power to detect SNPs in the meta-

analysis. Similarly, extraversion item data were harmo-

nized as much as possible (van den Berg et al. 2014), but

the Reward Dependence item data from the TCI were least

successfully linked to the extraversion data from the other

inventories. This may also have caused some loss in power.

Importantly however, it should be noted that by combining

genotype and phenotype data across cohorts as performed

in this study, a substantial increase in sample size was

obtained. It is nontrivial that the gain in power associated

with this increase in sample size largely outweighs any

potential loss in power due to any remaining genotyping or

phenotyping differences across cohorts.

In conclusion, extraversion is a heritable, highly poly-

genic personality trait with a genetic background that may

be qualitatively different from that of other complex

behavioral traits. Future studies are required to increase our

knowledge of which types of genetic variants, by which

modes of gene action, constitute the heritable nature of

extraversion. Ultimately, this knowledge can be used to

increase our understanding of how extraversion is related

to various important psychosocial and health outcomes.

Acknowledgments We would like to thank all participating sub-

jects. Analyses were carried out on the Genetic Cluster Computer

(http://www.geneticcluster.org), which is financially supported by the

Netherlands Organization for Scientific Research (NWO 480-05-003).

ALSPAC We are extremely grateful to all the families who took

part in this study, the midwives for their help in recruiting them and

the whole ALSPAC team, which includes interviewers, computer and

laboratory technicians, clerical workers, research scientists, volun-

teers, managers, receptionists and nurses. The UK Medical Research

Council (Grant 74882), the Wellcome Trust (Grant 076467) and the

University of Bristol provide core support for ALSPAC. We thank

23andMe for funding the genotyping of the ALSPAC children’s

sample. This publication is the work of the authors, and they will

serve as guarantors for the contents of this paper.

BLSA We acknowledge support from the Intramural Research

Program of the NIH, National Institute on Aging. We thank Robert

McCrae.

BRESCIA We acknowledge support from the Italian Ministry of

Health (RC and RF2007 Conv. 42) and Regione Lombardia (ID:

17387 SAL-13). We thank Ilaria Gandin for imputation analysis

support.

CHICAGO This work was supported by NIH Grants, DA007255

(ABH), HG006265 (to BEE), DA02812 (to HdW), and DA021336

and DA024845 (to AAP). BEE was also funded through the Bioin-

formatics Research Development Fund, supported by Kathryn and

George Gould. We wish to thank Andrew D. Skol for providing

advice about genotype calling.

CILENTO We acknowledge Dr Maria Enza Amendola for the test

administration and thank the personnel working in the organization of

the study in the villages. MC received funding support from the

Italian Ministry of Universities (FIRB - RBNE08NKH7, INTERO-

MICS Flaghip Project), the Assessorato Ricerca Regione Campania,

the Fondazione con il SUD (2011-PDR-13), and the Fondazione

Banco di Napoli.

SAGE – COGA/CONGEND Funding support for the Study of

Addiction: Genetics and Environment (SAGE) was provided through

the NIH Genes, Environment and Health Initiative [GEI] (U01

HG004422). SAGE is one of the genome-wide association studies

funded as part of the Gene Environment Association Studies (GEN-

EVA) under GEI. Assistance with phenotype harmonization and

genotype cleaning, as well as with general study coordination, was

provided by the GENEVA Coordinating Center (U01 HG004446).

Assistance with data cleaning was provided by the National Center

for Biotechnology Information. Support for collection of datasets and

samples was provided by the Collaborative Study on the Genetics of

Alcoholism (COGA; U10 AA008401) and the Collaborative Genetic

Study of Nicotine Dependence (COGEND; P01 CA089392). Funding

support for genotyping, which was performed at the Johns Hopkins

University Center for Inherited Disease Research, was provided by

the NIH GEI (U01HG004438), the National Institute on Alcohol

Abuse and Alcoholism, the National Institute on Drug Abuse, and the

NIH contract ‘‘High throughput genotyping for studying the genetic

contributions to human disease’’(HHSN268200782096C). The Col-

laborative Study on the Genetics of Alcoholism (COGA), Principal

Investigators B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut,

includes ten different centers: University of Connecticut (V. Hessel-

brock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T.

Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY

Downstate (B. Porjesz); Washington University in St. Louis (L.

Bierut, A. Goate, J. Rice, K. Bucholz); University of California at San

Diego (M. Schuckit); Rutgers University (J. Tischfield); Texas

Biomedical Research Institute (L. Almasy), Howard University (R.

Taylor) and Virginia Commonwealth University (D. Dick). Other

COGA collaborators include: L. Bauer (University of Connecticut);

D. Koller, S. O’Connor, L. Wetherill, X. Xuei (Indiana University);

Grace Chan (University of Iowa); S. Kang, N. Manz, M. Rangaswamy

(SUNY Downstate); J. Rohrbaugh, J-C Wang (Washington University

in St. Louis); A. Brooks (Rutgers University); and F. Aliev (Virginia

Commonwealth University). A. Parsian and M. Reilly are the NIAAA

Staff Collaborators. This national collaborative study is supported by

NIH Grant U10AA008401 from the National Institute on Alcohol

Abuse and Alcoholism (NIAAA) and the National Institute on Drug

Abuse (NIDA). The Collaborative Genetic Study of Nicotine

Dependence (COGEND) project is a collaborative research group and

part of the NIDA Genetics Consortium. Subject collection was sup-

ported by NIH Grant P01 CA089392 (L.J. Bierut) from the National

Cancer Institute. Phenotypic and genotypic data are stored in the

NIDA Center for Genetic Studies (NCGS) at http://zork.wustl.edu/

under NIDA Contract HHSN271200477451C (J. Tischfield and J.

Rice). Jaime Derringer was supported by NIH T32 MH016880.

EGCUT AM and TE received support from FP7 Grants (201413

ENGAGE, 212111 BBMRI, ECOGENE (No. 205419, EBC)) and

OpenGENE. AM and TE also received targeted financing from

Estonian Government SF0180142s08 and by EU via the European

Regional Development Fund, in the frame of Centre of Excellence in

Genomics. The genotyping of the Estonian Genome Project samples

were performed in Estonian Biocentre Genotyping Core Facility, AM

178 Behav Genet (2016) 46:170–182

123

and TE thank Mari Nelis and Viljo Soo for their contributions. AR

and JA were supported by a grant from the Estonian Ministry of

Science and Education (SF0180029s08).

ERF The ERF study as a part of EUROSPAN (European Special

Populations Research Network) was supported by European Com-

mission FP6 STRP Grant Number 018947 (LSHG-CT-2006-01947)

and also received funding from the European Community’s Seventh

Framework Programme (FP7/2007-2013)/Grant agreement

HEALTH-F4-2007-201413 by the European Commission under the

programme ‘‘Quality of Life and Management of the Living

Resources’’ of 5th Framework Programme (no. QLG2-CT-2002-

01254). The ERF study was further supported by ENGAGE consor-

tium and CMSB. High-throughput analysis of the ERF data was

supported by joint grant from Netherlands Organisation for Scientific

Research and the Russian Foundation for Basic Research (NWO-

RFBR 047.017.043). ERF was further supported by the ZonMw Grant

(Project 91111025). We are grateful to all study participants and their

relatives, general practitioners and neurologists for their contributions

and to P. Veraart for her help in genealogy, J. Vergeer for the

supervision of the laboratory work and P. Snijders for his help in data

collection.

Finnish Twin Cohort (FTC) We acknowledge support from the

Academy of Finland Center of Excellence in Complex Disease

Genetics (Grant Numbers: 213506, 129680), the Academy of Finland

(Grants 100499, 205585, 118555 and 141054 to JK, Grant 257075 to

EV), Global Research Awards for Nicotine Dependence (GRAND),

ENGAGE (European Network for Genetic and Genomic Epidemiol-

ogy, FP7-HEALTH-F4-2007, Grant Agreement Number 201413),

DA12854 to P A F Madden, and AA-12502, AA-00145, and AA-

09203 to RJRose, AA15416 and K02AA018755 to DM Dick.

HBCS We thank all study participants as well as everybody

involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort

Study has been supported by grants from the Academy of Finland, the

Finnish Diabetes Research Society, Folkhalsan Research Foundation,

Novo Nordisk Foundation, Finska Lakaresallskapet, Signe and Ane

Gyllenberg Foundation, University of Helsinki, Ministry of Educa-

tion, Ahokas Foundation, Emil Aaltonen Foundation.

CROATIA-Korcula The CROATIA-Korcula study was funded by

grants from the Medical Research Council (UK), European Com-

mission Framework 6 project EUROSPAN (Contract No. LSHG-CT-

2006-018947) and Republic of Croatia Ministry of Science, Educa-

tion and Sports research Grants to I.R. (108-1080315-0302). We

would like to acknowledge the invaluable contributions of the

recruitment team in Korcula, the administrative teams in Croatia and

Edinburgh and the people of Korcula. The SNP genotyping for the

CROATIA-Korcula cohort was performed in Helmholtz Zentrum

Munchen, Neuherberg, Germany.

LBC1921 & LBC1936 For the Lothian Birth Cohorts, we thank

Paul Redmond for database management; Alan Gow, Michelle Tay-

lor, Janie Corley, Caroline Brett and Caroline Cameron for data

collection and data entry; nurses and staff at the Wellcome Trust

Clinical Research Facility, where blood extraction and genotyping

was performed; staff at the Lothian Health Board, and the staff at the

SCRE Centre, University of Glasgow. The research was supported by

a program grant from Research Into Ageing. The research continues

with program grants from Age UK (The Disconnected Mind). The

work was undertaken by The University of Edinburgh Centre for

Cognitive Ageing and Cognitive Epidemiology, part of the cross

council Lifelong Health and Wellbeing Initiative (MR/K026992/1).

Funding from the Biotechnology and Biological Sciences Research

Council (BBSRC) and Medical Research Council (MRC) is gratefully

acknowledged. IJD, DJP and colleagues receive support from Well-

come Trust Strategic Award 104036/Z/14/Z.

MCTFR We would like to thank Rob Kirkpatrick for his help

running analyses.

Research reported in this publication was supported by the National

Institutes of Health under award numbers R37DA005147,

R01AA009367, R01AA011886, R01DA013240, R01MH066140, and

U01DA024417.

MGS Samples were collected under the following grants: NIMH

Schizophrenia Genetics Initiative U01s: MH46276, MH46289, and

MH46318; and Molecular Genetics of Schizophrenia Part 1 (MGS1)

and Part 2 (MGS2) R01s: MH67257, MH59588, MH59571,

MH59565, MH59587, MH60870, MH60879, MH59566, MH59586,

and MH61675. Genotyping and analyses were funded under the MGS

U01s: MH79469 and MH79470.

NBS Principal investigators of the Nijmegen Biomedical Study are

L.A.L.M. Kiemeney, M. den Heijer, A.L.M. Verbeek, D.W. Swinkels

and B. Franke.

NESDA The Netherlands Study of Depression and Anxiety

(NESDA) were funded by the Netherlands Organization for Scientific

Research (Geestkracht program Grant 10-000-1002); the Center for

Medical Systems Biology (CMSB, NWO Genomics), Biobanking and

Biomolecular Resources Research Infrastructure (BBMRI-NL), VU

University’s EMGO Institute for Health and Care Research and

Neuroscience Campus Amsterdam. Genotyping was funded by the US

National Institute of Mental Health (RC2MH089951) as part of the

American Recovery and Reinvestment Act of 2009. BP is financially

supported by NWO-VIDI Grant No. 91811602.

NTR We acknowledge financial support from the Netherlands

Organization for Scientific Research (NWO): Grants 575-25-006,

480-04-004, 904-61-090; 904-61-193, 400-05-717 and Spinozapremie

SPI 56-464-14192 and the European Research Council (ERC-

230374). MHMdeM is supported by NWO VENI Grant No. 016-115-

035. Genotyping was funded by the Genetic Association Information

Network (GAIN) of the Foundation for the US National Institutes of

Health, and analysis was supported by grants from Genetic Associ-

ation Information Network and the NIMH (MH081802). Genotype

data were obtained from dbGaP (http://www.ncbi.nlm.nih.gov/dbgap,

accession number phs000020.v1.p1).

ORCADES was supported by the Chief Scientist Office of the

Scottish Government, the Royal Society, the MRC Human Genetics

Unit, Arthritis Research UK and the European Union framework

program 6 EUROSPAN project (contract no. LSHG-CT-2006-

018947). DNA extractions were performed at the Wellcome Trust

Clinical Research Facility in Edinburgh. We would like to

acknowledge the research nurses in Orkney, the administrative team

in Edinburgh and the people of Orkney.

PAGES none.

QIMR Berghofer adolescents/adults We thank Marlene Grace and

Ann Eldridge for sample collection; Megan Campbell, Lisa Bowdler,

Steven Crooks and staff of the Molecular Epidemiology Laboratory

for sample processing and preparation; Harry Beeby, David Smyth

and Daniel Park for IT support. We acknowledge support from the

Australian Research Council Grants A79600334, A79906588,

A79801419, DP0212016, DP0343921, DP0664638, and DP1093900

(to NGM and MJW), Beyond Blue and the Borderline Personality

Disorder Research Foundation (to NGM), NIH Grants DA12854 (to

PAFM), AA07728, AA07580, AA11998, AA13320, AA13321 (to

ACH) and MH66206 (to WSS); and grants from the Australian

National Health and Medical Research Council; MLP is supported by

DA019951. Genotyping was partly funded by the National Health and

Medical Research Council (Medical Bioinformatics Genomics Pro-

teomics Program, 389891) and the 5th Framework Programme (FP-5)

GenomEUtwin Project (QLG2-CT-2002-01254). Further genotyping

at the Center for Inherited Disease Research was supported by a grant

to the late Richard Todd, M.D., Ph.D., former Principal Investigator

of Grant AA13320. SEM and GWM are supported by the National

Health and Medical Research Council Fellowship Scheme. Further,

we gratefully acknowledge Dr Dale R Nyholt for his substantial

Behav Genet (2016) 46:170–182 179

123

involvement in the QC and preparation of the QIMR GWA data sets.

Dr Nyholt also contributed 8 % of the GWAS for the QIMR adult

cohort (NHMRC IDs 339462, 442981, 389938, 496739).

SardiNIA We acknowledge support from the Intramural Research

Program of the NIH, National Institute on Aging. Funding was pro-

vided by the National Institute on Aging, NIH Contract No. NO1-AG-

1-2109 to the SardiNIA (‘ProgeNIA’) team.

SHIP SHIP is part of the Community Medicine Research net of the

University of Greifswald, Germany, which is funded by the Federal

Ministry of Education and Research (Grants No. 01ZZ9603,

01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the

Social Ministry of the Federal State of Mecklenburg-West Pomerania.

Genome-wide data have been supported by the Federal Ministry of

Education and Research (Grant No. 03ZIK012) and a joint grant from

Siemens Healthcare, Erlangen, Germany and the Federal State of

Mecklenburg-West Pomerania. The University of Greifswald is a

member of the ‘Center of Knowledge Interchange’ program of the

Siemens AG. This work was also funded by the German Research

Foundation (DFG: GR 1912/5-1).

STR The STR was supported by grants from the Ministry for

Higher Education, the Swedish Research Council (M-2005-1112 and

2009-2298), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-

2002-01254), NIH Grant DK U01-066134, The Swedish Foundation

for Strategic Research (SSF; ICA08-0047), the Swedish Heart–Lung

Foundation, the Royal Swedish Academy of Science, and ENGAGE

(within the European Union Seventh Framework Programme,

HEALTH-F4-2007-201413).

CROATIA-Vis The CROATIA-Vis study was funded by grants

from the Medical Research Council (UK) and Republic of Croatia

Ministry of Science, Education and Sports research Grants to I.R.

(108-1080315-0302). We would like to acknowledge the staff of

several institutions in Croatia that supported the field work, including

but not limited to The University of Split and Zagreb Medical

Schools, the Institute for Anthropological Research in Zagreb and

Croatian Institute for Public Health. The SNP genotyping for the

CROATIA-Vis cohort was performed in the core genotyping labo-

ratory of the Wellcome Trust Clinical Research Facility at the Wes-

tern General Hospital, Edinburgh, Scotland.

YFS The Young Finns Study has been financially supported by the

Academy of Finland (Grants 126925, 121584, 124282, 129378

(Salve), 117787 (Gendi), 41071 (Skidi), and 265869 (Mind)), the

Social Insurance Institution of Finland, Kuopio, Tampere and Turku

University Hospital Medical Funds (Grant 9N035 for Dr. Lehtimaki),

Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Founda-

tion of Cardiovascular Research and Finnish Cultural Foundation,

Tampere Tuberculosis Foundation and Emil Aaltonen Foundation (for

Dr. Lehtimaki). The expert technical assistance in statistical analysis

by Irina Lisinen, Mika Helminen, and Ville Aalto is gratefully

acknowledged.

GS:SHFHS GS:SFHS is funded by the Scottish Executive Health

Department, Chief Scientist Office, Grant Number CZD/16/6. Exome

array genotyping for GS:SFHS was funded by the Medical Research

Council UK and performed at the Wellcome Trust Clinical Research

Facility Genetics Core at Western General Hospital, Edinburgh, UK.

We would like to acknowledge the invaluable contributions of the

families who took part in the GS:SFHS, the general practitioners and

Scottish School of Primary Care for their help in recruiting them, and

the whole GS:SFHS team, which includes academic researchers, IT

staff, laboratory technicians, statisticians and research managers.

Authors’ contributions Writing group: SMvdB, MHMdeM,

KJHV, RFK, ML, AAV, LKM, JD, TE, DIB. Analytic group:

MHMdeM, SMvdB, KJHV, ML, AAV, LKM, JDe, TE, NA, SG,

NKH, ABH, JH, BK, JL, ML, MM, TT, ATeu, AV, JW, IOF, NT,

DME, TL, IS, EP, GRA, JM, HM, AA, MN. Study design and project

management: LF, LPR, JGE, AAP, GWM, MJW, PAFM, DP, AMin,

AP, DR, MC, IG, CH, IR, AMet, JK, IJD, KR, JFW, LKJ, JMH, HJG,

BWJHP, CMvD, DME, NLP, PTC, ATer, MMG, NGM, DIB, RFK,

AAV, GDS, TL, OTR, PKEM, KH, JMS, DS, GRA, HC, WGI, JDi.

Sample and phenotype data collection: BWJHP, AMet, AR, JA,

PAFM, ACH, NGM, MJW, KA, MN, LJB, JGE, LF, PTC, IG, AMH,

ATer, GDS, MGK, HdW, AAP, AKH, WSS, RS, DR, Amin, MJ,

LPR, LKJ, OTR, PKEM, EV, KH, AM, FB, OP, LZ. Data prepara-

tion: SMvdB, MHMdeM, JW, KGO, JJH, SEM, NKH, YM, TE, AR,

GD, ML, RG, AA, JD, EW, GD, BEE, COS, GH, KJHV, SDG, DEA,

TBB, JPK, NJT, BP, BK, CM, MJ, AL, ARS, ATer, DCL, HT.

Conflict of Interest The authors declare that they have no conflict

of interest.

Human and Animal Rights and Informed Consent All proce-

dures performed in studies involving human participants were in

accordance with the ethical standards of the institutional and/or

national research committee and with the 1964 Helsinki declaration

and its later amendments or comparable ethical standards. Informed

consent was obtained from all individual participants included in the

study.

Open Access This article is distributed under the terms of the

Creative Commons Attribution 4.0 International License (http://crea

tivecommons.org/licenses/by/4.0/), which permits unrestricted use,

distribution, and reproduction in any medium, provided you give

appropriate credit to the original author(s) and the source, provide a

link to the Creative Commons license, and indicate if changes were

made.

References

Amin N, Hottenga JJ, Hansell NK, Janssens ACJW, de Moor MHM,

Madden PAF, Zorkoltseva IV, Penninx BW, Terracciano A,

Uda M, Tanaka T, Esko T, Realo A, Ferrucci L, Luciano M,

Davies G, Metspalu A, Abecasis GR, Deary IJ, Raikkonen K,

Bierut LJ, Costa PT, Saviouk V, Zhu G, Kirichenko AV, Isaacs

A, Aulchenko YS, Willemsen G, Heath AC, Pergadia ML,

Medland SE, Axenovich TI, de Geus E, Montgomery GW,

Wright MJ, Oostra BA, Martin NG, Boomsma DI, van Duijn

CM (2013) Refining genome-wide linkage intervals using a

meta-analysis of genome-wide association studies identifies loci

influencing personality dimensions. Eur J Hum Genet

21(8):876–882

Benjamini Y, Hochberg Y (1995) Controlling the false discovery

rate—a practical and powerful approach to multiple testing. J R

Stat Soc Series B-Methodol 57(1):289–300

Beuten J, Ma JZ, Payne TJ, Dupont RT, Quezada P, Huang WH,

Crews KA, Li MD (2005) Significant association of BDNF

haplotypes in European-American male smokers but not in

European-American female or African-American smokers. Am J

Med Genet B 139b(1):73–80

Bouchard TJ, Loehlin JC (2001) Genes, evolution, and personality.

Behav Genet 31(3):243–273

Briley DA, Tucker-Drob EM (2014) Genetic and environmental

continuity in personality development: a meta-analysis. Psychol

Bull 140(5):1303–1331

Church AT (1994) Relating the tellegen and 5-factor models of

personality structure. J Pers Soc Psychol 67(5):898–909

Cloninger CR (1987) A systematic method for clinical description

and classification of personality variants—a proposal. Arch Gen

Psychiatry 44(6):573–588

180 Behav Genet (2016) 46:170–182

123

Cloninger CR, Svrakic DM, Przybeck TR (1993) A psychobiological

model of temperament and character. Arch Gen Psychiatry

50(12):975–990

Costa PT, McCrae RR (1992) Professional manual: revised NEO

personality inventory (NEO-PI-R) and NEO five-factor-inven-

tory (NEO-FFI). Psychol Assess Resour, Odessa

De Fruyt F, Van De Wiele L, Van Heeringen C (2000) Cloninger’s

psychobiological model of temperament and character and the

five-factor model of personality. Pers Indiv Differ 29(3):441–452

De Moor MHM, Beem AL, Stubbe JH, Boomsma DI, de Geus EJC

(2006) Regular exercise, anxiety, depression and personality: a

population-based study. Prev Med 42(4):273–279

De Moor MHM, Vink JM, van Beek JHDA, Geels LM, Bartels M, de

Geus EJC, Willemsen AHM, Boomsma DI (2011) Heritability of

problem drinking and the genetic overlap with personality in a

general population sample. Front Behav Psychiatr Genet 2:76

De Moor MH, Costa PT, Terracciano A, Krueger RF, de Geus EJ,

Toshiko T, Penninx BW, Esko T, Madden PA, Derringer J, Amin

N, Willemsen G, Hottenga JJ, Distel MA, Uda M, Sanna S,

Spinhoven P, Hartman CA, Sullivan P, Realo A, Allik J, Heath

AC, Pergadia ML, Agrawal A, Lin P, Grucza R, Nutile T, Ciullo

M, Rujescu D, Giegling I, Konte B, Widen E, Cousminer DL,

Eriksson JG, Palotie A, Peltonen L, Luciano M, Tenesa A,

Davies G, Lopez LM, Hansell NK, Medland SE, Ferrucci L,

Schlessinger D, Montgomery GW, Wright MJ, Aulchenko YS,

Janssens AC, Oostra BA, Metspalu A, Abecasis GR, Deary IJ,

Raikkonen K, Bierut LJ, Martin NG, van Duijn CM, Boomsma

DI (2012) Meta-analysis of genome-wide association studies for

personality. Mol Psychiatry 17(3):337–349

De Moor MHM, van den Berg SM, Consortium GoP, Boomsma DI

(2015) Meta-analysis of genome-wide association studies for

neuroticism, and the polygenic association with major depressive

disorder. JAMA Psychiatry 72:642–650

Distel MA, De Moor MHM, Boomsma DI (2009a) Dutch translation

of the Personality Assessment Inventory-Borderline features

scale (PAI-BOR): norms, factor structure and reliability. Psychol

Gezondh 37(1):38–46

Distel MA, Trull TJ, Willemsen G, Vink JM, Derom CA, Lynskey

MT, Martin NG, Boomsma DI (2009b) The Five Factor Model

of personality and borderline personality disorder: a genetic

analysis of comorbidity. Biol Psychiatry 66:1131–1138

Eysenck HJ, Eysenck SBG (1964) Eysenck personality inventory.

Educational and Industrial Testing Service, San Diego

Eysenck HJ, Eysenck SBG (1975) Manual of the eysenck personality

questionnaire. Hodder & Stoughton, London

Eysenck SBG, Eysenck HJ, Barrett P (1985) A revised version of the

psychoticism scale. Pers Indiv Differ 6(1):21–29

Farkas N, Lendeckel U, Dobrowolny H, Funke S, Steiner J, Keilhoff

G, Schmitt A, Bogerts B, Bernstein HG (2010) Reduced density

of ADAM 12-immunoreactive oligodendrocytes in the anterior

cingulate white matter of patients with schizophrenia. World J

Biol Psychiatry 11(3):556–566

Finkel D, McGue M (1997) Sex differences and nonadditivity in

heritability of the multidimensional personality questionnaire

scales. J Pers Soc Psychol 72(4):929–938

Furnham A, Nuygards S, Chamorro-Premuzic T (2013) Personality,

assessment methods and academic performance. Instr Sci

41(5):975–987

Jiao H, Arner P, Hoffstedt J, Brodin D, Dubern B, Czernichow S,

van’t Hooft F, Axelsson T, Pedersen O, Hansen T, Sorensen

TIA, Hebebrand J, Kere J, Dahlman-Wright K, Hamsten A,

Clement K, Dahlman I (2011) Genome wide association study

identifies KCNMA1 contributing to human obesity. BMC Med

Genomics 4:51

Judge TA, Rodell JB, Klinger RL, Simon LS, Crawford ER (2013)

Hierarchical representations of the five-factor model of

personality in predicting job performance: integrating three

organizing frameworks with two theoretical perspectives. J Appl

Psychol 98(6):875–925

Kandler C (2012) Nature and nurture in personality development: the

case of neuroticism and extraversion. Curr Dir Psychol Sci

21(5):290–296

Keller MC, Coventry WL, Heath AC, Martin NG (2005) Widespread

evidence for non-additive genetic variation in Cloninger’s and

Eysenck’s personality dimensions using a twin plus sibling

design. Behav Genet 35(6):707–721

Kolen MJ, Brennan RL (2004) Test equating, scaling, and linking:

methods and practices. Springer, New York

Kuang WJ, Sun RF, Zhu YS, Li SB (2011) A new single-nucleotide

mutation (rs362719) of the reelin (RELN) gene associated with

schizophrenia in female Chinese Han. Genet Mol Res

10(3):1650–1658

Lang UE, Sander T, Lohoff FW, Hellweg R, Bajbouj M, Winterer G,

Gallinat J (2007) Association of the met66 allele of brain-

derived neurotrophic factor (BDNF) with smoking. Psychophar-

macology (Berl) 190(4):433–439

Li MX, Kwan JS, Sham PC (2012) HYST: a hybrid set-based test for

genome-wide association studies, with application to protein-

protein interaction-based association analysis. Am J Hum Genet

91(3):478–488

Liu Q-R, Walther D, Drgon T, Polesskaya O, Lesnick TG, Strain KJ,

de Andrade M, Bower JH, Maraganore DM, Uhl GR (2005)

Human brain derived neurotrophic factor (BDNF) genes, splic-

ing patterns, and assessments of associations with substance

abuse and Parkinson’s Disease. Am J Med Genet B Neuropsy-

chiatr Genet 134B(1):93–103

Middeldorp CM, de Moor MH, McGrath LM, Gordon SD, Black-

wood DH, Costa PT, Terracciano A, Krueger RF, de Geus EJ,

Nyholt, Tanaka T, Esko T, Madden PA, Derringer J, Amin N,

Willemsen G, Hottenga JJ, Distel MA, Uda M, Sanna S,

Spinhoven P, Hartman CA, Ripke S, Sullivan PF, Realo A, Allik

J, Heath AC, Pergadia ML, Agrawal A, Lin P, Grucza RA,

Widen E, Cousminer DL, Eriksson JG, Palotie A, Barnett JH,

Lee PH, Luciano M, Tenesa A, Davies G, Lopez LM, Hansell

NK, Medland SE, Ferrucci L, Schlessinger D, Montgomery GW,

Wright MJ, Aulchenko YS, Janssens AC, Oostra BA, Metspalu

A, Abecasis GR, Deary IJ, Raikkonen K, Bierut LJ, Martin NG,

Wray NR, van Duijn CM, Smoller JW, Penninx BW, Boomsma

DI (2011) The genetic association between personality and

major depression or bipolar disorder. A polygenic score analysis

using genome-wide association data. Transl Psychiatry 1:e50

Nadri C, Bersudsky Y, Belmaker RH, Agam G (2007) Elevated

urinary ADAM12 protein levels in lithium-treated bipolar

patients. J Neural Transm 114(4):473–477

Ovadia G, Shifman S (2011) The genetic variation of RELN

expression in schizophrenia and bipolar disorder. PLoS One

6(5):e19955

Patrick CJ, Curtin JJ, Tellegen A (2002) Development and validation

of a brief form of the multidimensional personality question-

naire. Psychol Assess 14(2):150–163

Rettew DC, Rebollo-Mesa I, Hudziak JJ, Willemsen G, Boomsma DI

(2008) Non-additive and additive genetic effects on extraversion

in 3314 Dutch adolescent twins and their parents. Behav Genet

38(3):223–233

Rhodes RE, Smith NEI (2006) Personality correlates of physical

activity: a review and meta-analysis. Br J Sports Med

40(12):958–965

Service SK, Verweij KJ, Lahti J, Congdon E, Ekelund J, Hintsanen

M, Raikkonen K, Lehtimaki T, Kahonen M, Widen E, Taanila A,

Veijola J, Heath AC, Madden PA, Montgomery GW, Sabatti C,

Jarvelin MR, Palotie A, Raitakari O, Viikari J, Martin NG,

Eriksson JG, Keltikangas-Jarvinen L, Wray NR, Freimer NB

Behav Genet (2016) 46:170–182 181

123

(2012) A genome-wide meta-analysis of association studies of

Cloninger’s Temperament Scales. Transl Psychiatry 2:e116

Sutin AR, Ferrucci L, Zonderman AB, Terracciano A (2011)

Personality and obesity across the adult life span. J Pers Soc

Psychol 101(3):579–592

Terracciano A, Lockenhoff CE, Zonderman AB, Ferrucci L, Costa PT

(2008) Personality predictors of longevity: activity, emotional

stability, and conscientiousness. Psychosom Med 70(6):621–627

Terracciano A, Sanna S, Uda M, Deiana B, Usala G, Busonero F,

Maschio A, Scally M, Patriciu N, Chen WM, Distel MA,

Slagboom EP, Boomsma DI, Villafuerte S, Sliwerska E,

Burmeister M, Amin N, Janssens ACJW, van Duijn CM,

Schlessinger D, Abecasis GR, Costa PT (2010) Genome-wide

association scan for five major dimensions of personality. Mol

Psychiatry 15(6):647–656

Terracciano A, Sutin AR, An Y, O’Brien RJ, Ferrucci L, Zonderman

AB, Resnick SM (2014) Personality and risk of Alzheimer’s

disease: new data and meta-analysis. Alzheimer’s Dement J

Alzheimer’s Assoc 10(2):179–186

Turkheimer E, Pettersson E, Horn EE (2014) A phenotypic null

hypothesis for the genetics of personality. Annu Rev Psychol

65:515–540

van den Berg SM, de Moor MH, McGue M, Pettersson E, Terracciano

A, Verweij KJ, Amin N, Derringer J, Esko T, van Grootheest G,

Hansell NK, Huffman J, Konte B, Lahti J, Luciano M, Matteson

LK, Viktorin A, Wouda J, Agrawal A, Allik J, Bierut L, Broms

U, Campbell H, Smith GD, Eriksson JG, Ferrucci L, Franke B,

Fox JP, de Geus EJ, Giegling I, Gow AJ, Grucza R, Hartmann

AM, Heath AC, Heikkila K, Iacono WG, Janzing J, Jokela M,

Kiemeney L, Lehtimaki T, Madden PA, Magnusson PK,

Northstone K, Nutile T, Ouwens KG, Palotie A, Pattie A,

Pesonen AK, Polasek O, Pulkkinen L, Pulkki-Raback L,

Raitakari OT, Realo A, Rose RJ, Ruggiero D, Seppala I, Slutske

WS, Smyth DC, Sorice R, Starr JM, Sutin AR, Tanaka T,

Verhagen J, Vermeulen S, Vuoksimaa E, Widen E, Willemsen

G, Wright MJ, Zgaga L, Rujescu D, Metspalu A, Wilson JF,

Ciullo M, Hayward C, Rudan I, Deary IJ, Raikkonen K, Arias

Vasquez A, Costa PT, Keltikangas-Jarvinen L, van Duijn CM,

Penninx BW, Krueger RF, Evans DM, Kaprio J, Pedersen NL,

Martin NG, Boomsma DI (2014) Harmonization of Neuroticism

and Extraversion phenotypes across inventories and cohorts in

the Genetics of Personality Consortium: an application of Item

Response Theory. Behav Genet 44(4):295–313

Verweij KJ, Zietsch BP, Medland SE, Gordon SD, Benyamin B,

Nyholt DR, McEvoy BP, Sullivan PF, Heath AC, Madden PA,

Henders AK, Montgomery GW, Martin NG, Wray NR (2010) A

genome-wide association study of Cloninger’s temperament

scales: implications for the evolutionary genetics of personality.

Biol Psychol 85(2):306–317

Verweij KJ, Yang J, Lahti J, Veijola J, Hintsanen M, Pulkki-Raback

L, Heinonen K, Pouta A, Pesonen AK, Widen E, Taanila A,

Isohanni M, Miettunen J, Palotie A, Penke L, Service SK, Heath

AC, Montgomery GW, Raitakari O, Kahonen M, Viikari J,

Raikkonen K, Eriksson JG, Keltikangas-Jarvinen L, Lehtimaki

T, Martin NG, Jarvelin, Visscher PM, Keller MC, Zietsch BP

(2012) Maintenance of genetic variation in human personality:

testing evolutionary models by estimating heritability due to

common causal variants and investigating the effect of distant

inbreeding. Evolution 66(10):3238–3251

Vilhjalmsson B, Yang J, Finucane HK, Gusev A, Lindstrom S, Ripke

S, Genovese G, Loh P-R, Bhatia G, Do R, Hayeck T, Won H-H,

Genomics Consortium SWGotP, Variants in Breast Cancer study

tDB, Risk of I, Kathiresan S, Pato M, Pato C, Tamimi R, Stahl E,

Zaitlen N, Pasaniuc B, Schierup M, De Jager P, Patsopoulos N,

McCarroll SA, Daly M, Purcell S, Chasman D, Neale B,

Goddard M, Visscher PM, Kraft P, Patterson NJ, Price AL

(2015) Modeling Linkage Disequilibrium Increases Accuracy of

Polygenic Risk Scores. bioRxiv. doi:10.1101/015859

Vinkhuyzen AA, Pedersen NL, Yang J, Lee SH, Magnusson PK,

Iacono WG, McGue M, Madden PA, Heath AC, Luciano M,

Payton A, Horan M, Ollier W, Pendleton N, Deary IJ,

Montgomery GW, Martin NG, Visscher PM, Wray NR (2012)

Common SNPs explain some of the variation in the personality

dimensions of neuroticism and extraversion. Transl Psychiatry

2:e102

Visscher PM, Yang J, Goddard ME (2010) A commentary on

‘Common SNPs explain a large proportion of the heritability for

human height’ by Yang et al. (2010). Twin Res Human Genet

13(6):517–524

Weiss A, Bates TC, Luciano M (2008) Happiness is a personal(ity)

thing—the genetics of personality and well-being in a represen-

tative sample. Psychol Sci 19(3):205–210

Yamagata S, Suzuki A, Ando J, Ono Y, Kijima N, Yoshimura K,

Ostendorf F, Angleitner A, Riemann R, Spinath FM, Livesley

WJ, Jang KL (2006) Is the genetic structure of human

personality universal? A cross-cultural twin study from North

America, Europe, and Asia. J Pers Soc Psychol 90(6):987–998

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt

DR, Madden PA, Heath AC, Martin NG, Montgomery GW,

Goddard ME, Visscher PM (2010) Common SNPs explain a

large proportion of the heritability for human height. Nat Genet

42(7):565–569

182 Behav Genet (2016) 46:170–182

123